Senior Machine Learning Engineer

This is a fully remote position, open to applicants in Brazil.

📋 Description

• ML Model Development: Design, build, and implement scalable machine learning models to address intricate business challenges.

• MLOps & Production: Establish and sustain robust ML pipelines, ensuring the deployment, monitoring, and maintenance of models in production environments.

• Feature Engineering: Develop and refine features utilizing dbt and PySpark, managing large datasets effectively.

• Workflow Orchestration: Create and oversee data and ML pipelines using Apache Airflow.

• Data Processing: Conduct large-scale distributed data processing with PySpark.

• Collaboration: Partner closely with data scientists, data engineers, and product teams to deliver comprehensive solutions.

• Optimization: Track model performance, detect degradation, and execute ongoing enhancements.

• Documentation: Ensure clear technical documentation of architecture, models, and processes is maintained.

⛳️ Requirements

• Demonstrated experience in developing and deploying machine learning models within production settings.

• Proficiency in Python and ML/DL libraries (such as scikit-learn, TensorFlow, PyTorch, XGBoost, etc.).

• Familiarity with GenAI architectures, including Amazon Bedrock or similar, RAG pipelines, vector databases (pgvector, OpenSearch, Pinecone), and LLM API integration.

• Expertise in API and microservices architecture (FastAPI, API Gateway, ECS/EKS).

• Proven experience in distributed data processing using PySpark.

• Advanced SQL skills and experience with PostgreSQL.

• Experience with Apache Airflow in building and managing complex DAGs.

• Knowledge of AWS Cloud services such as SageMaker, S3, EC2, Lambda, and ECR/ECS.

• Familiarity with Snowflake for analytics storage and processing.

• Understanding of dbt for data transformation and modeling.

• Proficiency in code versioning with Git and sound development practices.

• Nice to have: Experience with MLflow, Kubeflow, or other MLOps platforms.

• Knowledge of Docker and Kubernetes.

• Experience with Feature Stores (Feast, Tecton, etc.).

• Awareness of CI/CD practices for machine learning.

• Familiarity with Big Data technologies (Hadoop, Kafka, Spark Streaming).

• Experience with A/B testing and experimentation.

• AWS certifications (ML Specialty, Solutions Architect, etc.).

• Experience working with agile methodologies (Scrum, Kanban).

🏝️ Benefits

• Health and dental insurance.

• Meal and food allowance.

• Childcare assistance.

• Extended parental leave.

• Partnerships with gyms and health/wellness professionals through Wellhub (Gympass) and TotalPass.

• Profit-sharing (PLR).

• Life insurance.

• Access to a continuous learning platform (CI&T University).

• Employee discount club.

• Free online resources focused on physical and mental health and well-being.

• Courses on pregnancy and responsible parenting.

• Collaborations with online course platforms.

• Language learning platform.

• And many more.